How to Create Training Data Set for Machine Learning & AI in Agriculture?
AI is already playing a significant role in various fields, similarly, agriculture is the field where it can be implemented through various applications, system or machines that can perform various actions independently or analyze the useful data for better farming and agriculture.
In agriculture you can use automated tractors, drones, robots and other computer vision based machines to visualize the various scenario and help agriculture sector boos the productivity. Robots or AI drones are trained through machine learning algorithms, and to train the algorithms, you need certain amount of data sets containing the annotated images of crops and harvestings.
Training Data Set for Machine Learning & AI in Agriculture
To train the robots, you need the data set that contains the object of interest which could be either crops, fruits and vegetables are annotated with various techniques like bounding box to detect the object precisely. Anolytics provides the bounding box annotation for crop detection, fruit detection and unwanted crops in agriculture creating the high-quality data set for robots and autonomous machines.
Training Data for Robots in Agriculture and Farming
You can create the set of data for AI robots in agriculture with annotated fruits, vegetables showing the actual condition of such plants, and take action like plucking, spraying pesticides or detecting the weeds or unwanted crops to remove them from the fields that are eating the nutrition of soil. Using the training data sets for harvesting and crop controlling is possible when you create a right data.
Training Data for Aerial View Mapping of Agricultural Field
Another type of data you need for AI in agriculture is for drones that can provides the useful details of agricultural fields to check the soil condition through geo sensing and monitor the health of the crops. In farming sector, AI drones can monitor the live stocks like cow, buffalos, sheep and other creatures used in animal husbandry. Only such high-quality training data can help drones to learn from such data sets.
Training Data for Live Stock Management in Farming
For livestock management drones are used to monitor the animals grazing the grass in the open field. Hence, to make these objects and animals, recognizable to machines (drones) you need to create the data set containing the annotated fields and different types of animals. Semantic segmentation and polygon annotation are the image annotation technique used to create the training data for AI.
Trending AI Articles:
1. Making a Simple Neural Network
Similarly, drones and robots can be trained to perform various tasks like spraying the pesticides, aerial view monitoring of crops to prevent from harmful insects and other animals. Similarly, AI based various applications can also provide the details to grow the crop in better way and improve the yield.
Making the all types of objects including crops, fruits, vegetables and other things in the agricultural filed need to annotated and feed into the machine learning algorithms for agriculture to make the model visualize various situations and take the action accordingly. Semantic segmentation is also one of the most crucial image annotation technique for deep learning in agriculture.
Anolytics provide the high-quality training data for machine learning and AI in agriculture. It can provide you the annotated images of all types of objects in the agricultural field with best level of accuracy, It is specialized in image annotation services to provide the machine learning training data sets to develop the different types of AI models with high rate of success when used in real-life.
Don’t forget to give us your 👏 !
